In November 2019, the coronavirus disease outbreak began, caused by the novel severe acute respiratory syndrome coronavirus 2. In just over two months, the unprecedented rapid spread resulted in more than 10,000 confirmed cases worldwide. This study predicted the infectious spread of coronavirus disease in the contiguous United States using a convolutional autoencoder with long short-term memory and compared its predictive performance with that of the convolutional autoencoder without long short-term memory. The epidemic data were obtained from the World Health Organization and the US Centers for Disease Control and Prevention from January 1st to April 6th, 2020. We used data from the first 366,607 confirmed cases in the United States. In this study, the data from the Centers for Disease Control and Prevention were gridded by latitude and longitude and the grids were categorized into six epidemic levels based on the number of confirmed cases. The input of the convolutional autoencoder with long short-term memory was the distribution of confirmed cases 14 days before, whereas the output was the distribution of confirmed cases 7 days after the date of testing. The mean square error in this model was 1.664, the peak signal-to-noise ratio was 55.699, and the structural similarity index was 0.99, which were better than those of the corresponding results of the convolutional autoencoder. These results showed that the convolutional autoencoder with long short-term memory effectively and reliably predicted the spread of infectious disease in the contiguous United States.

在2019年11月，由新型严重急性呼吸系统综合症冠状病毒2引起的冠状病毒疾病爆发开始。在短短两个多月中，空前的快速传播导致全世界10,000例确诊病例。这项研究使用具有长短期记忆的卷积自动编码器预测了连续性美国冠状病毒疾病的传染性传播，并将其预测性能与没有长期短期记忆的卷积自动编码器的预测性能进行了比较。该流行病数据是从2020年1月1日至4月6日从世界卫生组织和美国疾病控制与预防中心获得的。我们使用了来自美国首批366,607例确诊病例的数据。在这项研究中，疾病预防控制中心的数据按经度和纬度进行网格划分，并根据确诊病例数将网格划分为六个流行等级。具有短时记忆的卷积自动编码器的输入是在测试日期前14天的确诊病例分布，而输出是在测试日期后7天的确诊病例的分布。该模型的均方误差为1.664，峰值信噪比为55.699，结构相似指数为0.99，优于卷积自编码器的相应结果。这些结果表明，具有长短期记忆的卷积自动编码器有效而可靠地预测了美国附近传染病的蔓延。